The Effectiveness of Mobile Assisted Language Learning (MALL) on ESL Listening Skill
Bibliographic record
Abstract
Using mobile technology in English learning and teaching has been on the rise all over the world over the past few decades and hence, has received considerable attention and importance from academics in recent years. As a result, several experimental studies have been carried out about the use and effectiveness of mobile phones in the teaching/learning process. However, there have been only a few studies on mobile-assisted listening comprehension. This study aims to explore whether Mobile Assisted Language Learning (MALL) is effective in teaching/learning listening skills to the students of university-level English language programs and could better enhance students’ listening ability. It also endeavors to assess why some MALL strategies/techniques are more effective than others. This study uses a qualitative research method. It exclusively uses the relevant secondary materials available on the broader topic- the use and efficacy of mobile phones in teaching/learning listening skills. The results indicated that the MALL is meaningfully efficacious in teaching/learning ESL/EFL listening skills. Therefore, using appropriate strategies could positively contribute to bringing about better learning. Besides outlining a brief overview of MALL, the study also recommends some practical and useful stratagems that ESL/EFL educators can use while designing MALL listening tasks/activities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".